AI Prompts for Python Development

Before: The Weak Prompt

A data engineer named Priya spent an afternoon building a Python script to process CSV files and load them into a database. She used AI throughout — it was fast and the code looked clean. She pushed it to production on a Friday afternoon.

By Monday, the pipeline had silently dropped 4,200 rows. No error. No log message. Just missing data.

The root cause: the AI-generated code used a bare

except Exception

Her AI prompt had been:

Write a Python script to read a CSV file and insert each row into a PostgreSQL database table.

CSV columns: user_id, email, signup_date, plan_type
Table: users

The AI wrote working code. It just wasn't production-safe code. And she hadn't asked for that distinction.

Why It Fails

"Write a Python script to do X" is the most common ai prompts python development pattern and one of the most dangerous for production use. Here's why:

No error handling requirements. Python scripts that run in production need to handle network failures, malformed data, database connection drops, and partial failures gracefully. "Write me a script" produces code that handles the happy path.

No logging requirements. Production Python needs structured logging at appropriate levels. Silent scripts are debugging nightmares. The AI doesn't add logging unless you ask.

No idempotency consideration. If the script runs twice, does it insert duplicates? "Write me a script" won't raise this question.

No context on where this runs. A script running in a Kubernetes cron job has different failure handling needs than one running interactively. Lambda functions have a 15-minute timeout. Airflow DAGs need specific exception types. Context changes the code.

⚠️ Common mistake: Accepting AI-generated Python without specifying production requirements. Development code and production code look similar on the surface. They fail very differently.

After: The Improved Prompt

Write a production-ready Python 3.11 script for the following data pipeline task.

Task: Read a CSV file and upsert each row into a PostgreSQL users table.

CSV columns: user_id (int), email (str), signup_date (date, format YYYY-MM-DD), plan_type (str, one of: free|pro|enterprise)

Production requirements:
1. Structured logging with Python's logging module — log at DEBUG for each row, INFO for batch summaries, ERROR for failures
2. Error handling: malformed rows should be logged and written to a separate error CSV, not crash the script
3. Idempotency: use INSERT ... ON CONFLICT DO UPDATE so re-running is safe
4. Progress tracking: log total processed, total succeeded, total failed at the end
5. Database connection: use psycopg2 with connection pooling, handle connection drops with retry (max 3 attempts, exponential backoff)
6. Config: database credentials from environment variables (never hardcoded)
7. Input validation: validate email format, date format, and plan_type values before insert — invalid rows go to error CSV

Runtime context: runs as a scheduled Kubernetes cron job, invoked via command line with CSV path as argument

Include: a brief docstring at the top describing the script's purpose, inputs, and outputs.

What this does: Explicitly lists every production requirement — error handling, logging, idempotency, input validation, and configuration management — so the AI generates code that's actually deployable, not just runnable.

⚡ Pro tip: Always specify "credentials from environment variables (never hardcoded)" in any Python script prompt that touches databases, APIs, or cloud services. AI will sometimes hardcode placeholder credentials without this instruction — and placeholder credentials become real credentials in the worst way when someone edits the script.

Breaking Down Each Element

"Production-ready Python 3.11" — version matters. f-strings, walrus operator, match statements, and some standard library modules behave differently across versions. Specifying 3.11 gets you code that uses modern idioms and doesn't accidentally use deprecated patterns.

Structured logging requirements — specifying log levels (DEBUG, INFO, ERROR) ensures you can filter log output meaningfully in production. "Add logging" without levels gives you print statements at best.

"Malformed rows to error CSV" — this is the key fix for Priya's problem. Instead of swallowing errors silently, the script now quarantines bad data where humans can inspect it. This is the difference between a pipeline you can trust and one you have to audit after every run.

Idempotency via ON CONFLICT — a two-word instruction that prevents an entire class of production incident: duplicate data from script restarts.

Exponential backoff on connection drops — production database connections fail. Environments restart. Specifying retry behavior with exponential backoff prevents both too-aggressive retry storms and too-passive abandonment on transient failures.

"Kubernetes cron job invoked via command line" — this context changes the argument-parsing approach (argparse vs sys.argv vs config file), the exit code handling (cron jobs need proper exit codes for failure detection), and the logging destination (stdout for cron, so the orchestrator captures it).

⚡ Pro tip: After getting the script, ask: "Write a test that simulates a CSV with 3 valid rows, 1 row with an invalid email, and 1 row with an invalid plan_type. Verify the error CSV contains exactly the 2 invalid rows and the database contains exactly the 3 valid rows." This immediately validates the error handling behavior.

Variations for Different Contexts

For async Python (asyncio/aiohttp):

Write a production-ready async Python 3.11 script using asyncio. Include: proper exception handling in async context (avoid swallowing exceptions in gather calls), structured logging compatible with async execution, graceful shutdown on SIGTERM, and semaphore-based concurrency limiting to avoid overwhelming downstream services.

What this does: Async Python has its own failure modes — especially around unhandled exceptions in coroutines that get silently dropped. This prompt addresses them explicitly.

For data processing with pandas:

Write a production-ready Python data processing script using pandas. Include: memory-efficient chunked reading for large files (chunksize parameter), dtype specifications to prevent type inference surprises, explicit handling of NaN vs None semantics, and progress logging per chunk.

What this does: Pandas' default type inference causes subtle bugs (dates parsed as strings, integers read as floats) that only surface when the data doesn't match assumptions. Explicit dtypes prevent silent corruption.

For AWS Lambda functions:

Write a Python 3.11 AWS Lambda function handler for [task]. Include: proper cold start optimization (move expensive imports outside the handler), structured logging with aws_lambda_powertools, error handling that distinguishes between retryable and non-retryable errors (raise for retryable, return error response for non-retryable), and respect the 15-minute Lambda timeout with appropriate progress checkpointing.

What this does: Lambda-specific constraints — cold starts, timeout limits, retry behavior — require specific code patterns that generic Python prompts don't address.

Save and Reuse This

The improved prompt structure — task + production requirements + runtime context — is the foundation for every reliable Python script. The production requirements section changes based on your environment; the principle doesn't.

Save your team's standard Python script prompt — including your specific logging format, database library preferences, and retry patterns — in a shared prompt library. PromptABCD works well for this: your ai prompts python development templates are one click away when the next pipeline task lands in your sprint.

Python Type Safety and Code Quality

One dimension that improves Python code quality significantly and that most Python prompts skip: type hints.

Add comprehensive type hints to the following Python code using Python 3.11 type annotation syntax. Include:
- Function parameter types and return types
- TypedDict for dictionary structures with known shapes
- Optional vs Union types where appropriate
- Type hints for async functions and generators

After adding types, identify any places where the type hints reveal a potential runtime error (e.g., a function that sometimes returns None but the caller doesn't handle it).

Code: [paste]

What this does: Type hints aren't just documentation — they're a bug detection tool. The process of adding them often surfaces logical errors that were invisible in untyped code. And "identify places where types reveal runtime errors" turns the type-addition pass into a code review pass.

⚡ Pro tip: Ask AI to generate a mypy configuration file alongside type-annotated code: "Write a mypy.ini configuration for this project that enforces strict mode for new code and permissive mode for legacy modules in the 'legacy/' directory." This gives you a gradual typing adoption path instead of an all-or-nothing commitment.

For larger Python projects, AI-assisted refactoring prompts compound over time. Each sprint, pick one module and run: "Refactor this module for production-readiness: add type hints, replace bare excepts with specific exception handling, add structured logging, and extract any magic numbers into named constants." After ten sprints, the codebase looks like it was written by a team with consistent standards — because the AI enforced them.

Building these prompts into your PromptABCD library means the next developer on the team inherits a system, not a blank page. That's the compounding benefit of systematic ai prompts python development.

Start this sprint. Pick one script.

⚡ Pro tip: For teams working with multiple database types, save a debugging prompt variant for each: PostgreSQL-specific prompt, MySQL-specific, SQLite-specific. Database-specific debugging often requires database-specific tooling knowledge — and your prompt should reflect that. A query that's slow in PostgreSQL may have a different root cause than the same query in MySQL, even if the SQL looks identical.

The initial investment in a solid debugging prompt